Sensitivity of mass transfer-based evaporation equations to errors in daily and monthly input data

The influence of data errors on the performance of mass transfer-based evaporation equations was investigated for both monthly and daily data from a climatological station in the State of Vaud in Switzerland. Evaporation estimates were found to be particularly sensitive to vapour pressure gradient, less sensitive to wind speed and most insensitive to temperature. A quantitative analysis showed that: (1) systematic errors in vapour pressure data influenced evaporation estimates inversely to more or less the same magnitude for both monthly and daily cases; (2) systematic errors in wind speed and temperature data influenced evaporation estimates inversely to the magnitude of about a half and a quarter of the magnitude of the influence of vapour pressure errors, respectively; and (3) evaporation estimates were much more sensitive to random errors in the case of monthly data than daily data. © 1997 John Wiley & Sons, Ltd.     

ESTIMATION OF TEMPORAL CHANGES IN SOIL MOISTURE USING RESISTIVITY METHOD

The temporal variation in a soil moisture profile can be studied using resistivity sounding data acquired at different times. The layered earth model based estimation of soil moisture from apparent resistivity data is a two-step non-linear inversion. Firstly, the apparent resistivity data are inverted to derive the layer resistivity variations and thicknesses and, secondly, the moisture content is estimated from these layer resistivity variations using a calibration equation. The soil moisture–resistivity problem was studied using the one-dimensional formulation of resistivity problem. A generalized geoelectric earth model was considered to simulate the soil moisture distribution and its temporal variation in the unsaturated zone. An algorithm (RESMOS) for the interpretation of the apparent resistivity data in terms of soil moisture variations through this two-step inversion process is reported.     

Distribution of geochemical fractions of phosphorus and its ecological risk in sediment cores of a largest brackish water lake,South Asia

The current study focuses on the vertical profile of different geochemical fractions of phosphorus-loosely bound(Lo–P),aluminium bound(Al–P),iron bound(Fe–P),calcium bound(Ca–P),and organic bound phosphorus(O–P)along with ecological risk assessment of sediment cores from Chilika Lake,eastcoast of India.The percentage contribution of the different fractions to the sedimentary phosphorus in the sediment column of the whole lake are on the order:O–P(33.2%)>Ca–P(20.3%)>Fe–P(18%)>Al–P(6.7%)>Lo–P(0.35%).The Phosphorus Pollution Index(PPI)revealed the contamination of lake sediment with phosphorus.The principal component and cluster analyses highlighted the anthropogenic contribution of phosphorus.The negative loading of Ca–P with Ca points towards its origin from marine shells.The discriminate analysis showed that the variables like Ca–P,bio-available phosphorus(BAP),and pH were able to effectively discriminate the sectors in a significant manner.     

Groundwater Quality Assessment in a Hyper-arid Region of Rajasthan,India

Groundwater is an important source of livelihood in regions where rainfall is scanty, surface water sources are absent, and all domestic and agricultural needs are fulfilled with groundwater. This study deals with groundwater quality assessment in a hyper-arid region using multivariate statistical analysis. A total of 43 samples were collected and analyzed using principal component analysis and hierarchical cluster analysis to model the relationship and interdependence among the various physicochemical variables contributing to groundwater quality in the study area. The results of the statistical techniques showed that the variables are in strong correlation with each other. Cluster analysis proved to be a good tool to ascertain the spatial similarity between the contributing variables. The methodology adopted in the present study has been found to be effective and can be utilized to establish strong water quality monitoring network in similar areas.

     

Snow and glacier melt in the Satluj River at Bhakra Dam in the western Himalayan region

Abstract

Streamflow in the Himalayan rivers is generated from rainfall, snow and ice. The distribution of runoff produced from these sources is such that the streamflow may be observed in these rivers throughout the year, i.e. they are perennial in nature. Snow and glacier melt runoff contributes substantially to the annual flows of these rivers and its estimation is required for the planning, development and management of the water resources of this region. The average contribution of snow and glacier melt runoff in the annual flows of the Satluj River at Bhakra Dam has been determined. Keeping in view the availability of data for the study basin, a water balance approach was used and a water budget period of 10 years (October 1986-September 1996) was considered for the analysis. The rainfall input to the study basin over the water budget period was computed from isohyets using rainfall data of 10 stations located at different elevations in the basin. The total volume of flow for the same period was computed using observed flow data of the Satluj River at Bhakra Dam. A relationship between temperature and evaporation was developed and used to estimate the evapotranspiration losses. The snow-covered area, and its depletion with time, was determined using satellite data. It was found that the average contribution of snow and glacier runoff in the annual flow of the Satluj River at Bhakra Dam is about 59%, the remaining 41% being from rain.     

Air Pollution Modeling from Remotely Sensed Data Using Regression Techniques

There is a need for timely information about changes in the air pollution levels in cities for adopting precautionary measures. Keeping this in view, an attempt has been made to develop a model which will be useful to obtain air quality information directly from remotely sensed data easily and quickly. For this study pixel values, vegetation indices and urbanization index from IRS P6 LISS IV and Landsat ETM+ images were used to develop regression based models with Air Pollution Index (API), which were calculated from in-situ air pollutant information. It was found that among the 12 parameters of IRS, highest correlation exists between pixel values in NIR (Near Infra-Red) band (Pearson correlation ?0.77) and Normalized Difference Vegetation Index (NDVI) (Pearson correlation ?0.68) and both have inverse relationship with API. In case of Landsat, the highest correlation was observed in SWIR (Short Wave Infra-Red) band (Pearson correlation ?0.83) and NIR (Pearson correlation ?0.78). Both single and multivariate regression models were calibrated from best correlated variables from IRS and Landsat. Among all the models, multivariate regression model from Landsat with four most correlated variables gave the most accurate air pollution image. On comparison between the API modeled and API interpolated images, 90.5 % accuracy was obtained.